@InProceedings{6952067,
  author = {H. Asgharimoghaddam and A. Tölli and N. Rajatheva},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Decentralized multi-cell beamforming via large system analysis in correlated channels},
  year = {2014},
  pages = {341-345},
  abstract = {The optimal decentralization of multi-cell minimum power beamforming requires exchange of terms related to instantaneous inter-cell interference (ICI) values or channel state information (CSI) via a backhaul link. This limits the achievable performance in the limited backhaul capacity scenarios, especially when dealing with a fast fading scenario or a large number of users and antennas. In this work, we utilize the results from random matrix theory for developing two algorithms based on uplink-downlink duality and optimization decomposition relying on limited cooperation between nodes to share knowledge about channel statistics. As a result, approximately optimal power allocations are achieved based on statistics of the channels with greatly reduced backhaul information exchange rate. The simulations show that the performance gap due to the approximations is small even when the problem dimensions are relatively small.},
  keywords = {array signal processing;correlation methods;matrix algebra;optimisation;optimal decentralization;multicell minimum power beamforming;large system analysis;correlated channels;instantaneous intercell interference values;ICI values;channel state information;CSI;backhaul link;random matrix theory;uplink-downlink duality;optimization decomposition;channel statistics;optimal power allocations;backhaul information exchange rate reduction;performance gap;Approximation methods;Interference;Approximation algorithms;Signal to noise ratio;Array signal processing;Correlation;Antennas},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926495.pdf},
}

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This limits the achievable performance in the limited backhaul capacity scenarios, especially when dealing with a fast fading scenario or a large number of users and antennas. In this work, we utilize the results from random matrix theory for developing two algorithms based on uplink-downlink duality and optimization decomposition relying on limited cooperation between nodes to share knowledge about channel statistics. As a result, approximately optimal power allocations are achieved based on statistics of the channels with greatly reduced backhaul information exchange rate. The simulations show that the performance gap due to the approximations is small even when the problem dimensions are relatively small.","keywords":"array signal processing;correlation methods;matrix algebra;optimisation;optimal decentralization;multicell minimum power beamforming;large system analysis;correlated channels;instantaneous intercell interference values;ICI values;channel state information;CSI;backhaul link;random matrix theory;uplink-downlink duality;optimization decomposition;channel statistics;optimal power allocations;backhaul information exchange rate reduction;performance gap;Approximation methods;Interference;Approximation algorithms;Signal to noise ratio;Array signal processing;Correlation;Antennas","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926495.pdf","bibtex":"@InProceedings{6952067,\n author = {H. Asgharimoghaddam and A. Tölli and N. Rajatheva},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Decentralized multi-cell beamforming via large system analysis in correlated channels},\n year = {2014},\n pages = {341-345},\n abstract = {The optimal decentralization of multi-cell minimum power beamforming requires exchange of terms related to instantaneous inter-cell interference (ICI) values or channel state information (CSI) via a backhaul link. This limits the achievable performance in the limited backhaul capacity scenarios, especially when dealing with a fast fading scenario or a large number of users and antennas. In this work, we utilize the results from random matrix theory for developing two algorithms based on uplink-downlink duality and optimization decomposition relying on limited cooperation between nodes to share knowledge about channel statistics. As a result, approximately optimal power allocations are achieved based on statistics of the channels with greatly reduced backhaul information exchange rate. 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